Scheduling optimization in multiuser detection based MAC design for Ad-Hoc networks
Why this work is in the frame
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Bibliographic record
Abstract
Multiuser detection based Medium Access Control (MAC) can give significant gains in throughput and Quality of Service (QoS) when applied to wireless Ad Hoc networks. To realize these gains, one has to implement a distributed neighborhood scheduling that provides the desired performance objectives. In this paper, we propose an approach for analyzing and comparing optimal or suboptimal distributed neighborhood scheduling schemes with different objectives. Then, we demonstrate the viability of this approach by implementing a scheduling scheme that uses Start Time Fair Queuing (STFQ) algorithm and by comparing its performance to a published suboptimal distributed scheduling for multiuser detection based MAC. In particular, the numerical results show that the delay performance of the priority voice packets can be significantly improved by using STFQ algorithm.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it